Clustering process for analyzing pressure gradient data

摘要

Clustering analysis is used to partition data into similarity groups based on mathematical relationships between the measured variables. These relationships (or prototypes) are derived from the specific correlation required between the measured variables (data) and an environmental property of interest. The data points are partitioned into the prototype-driven groups (i.e., clusters) based on error minimization. Once the data is grouped, quantitative predictions and sensitivity analysis of the property of interest can be derived based on the computed prototypes. Additionally, the process inherently minimizes prediction errors due to the rigorous error minimization during data clustering while avoiding overfitting via algorithm parameterization. The application used to demonstrate the power of the method is pressure gradient analysis.

主权项

1. A measurement clustering method, comprising:
obtaining measurements of an environment, each measurement being multi-dimensional consisting of a plurality of variables in the environment; obtaining one or more prototypes defining expected relationships between the variables for predicting one or more physical properties of the environment; assigning the measurements in one or more clusters to the one or more prototypes; determining if at least one of the one or more clusters of the measurements violates an error constraint with respect to its original prototype and reassigning the measurements of the at least one cluster to new prototypes; recursively repeating the acts of determining and reassigning based on the error constraint determination; fitting each of the prototypes to its cluster of assigned measurements; and predicting the one or more physical properties of the environment based on the characteristics of the one or more cluster-fitted prototypes relating the variables in the environment.